There is known a testing method of inspecting whether a building wall is delaminated or peeled off by hearing sound with ears that is generated by hammering the wall. Moreover, there is widely known a method of hearing sound to check whether a fruit such as watermelon is ripe or not by tapping on same.
Such method of estimating an internal state of a test object by hitting the test object to impart vibrations thereto and hearing sound generated by the vibrations with ears, is widely applied in various fields.
There is also widely known a technology of ultrasonic diagnosis, which examines an internal state of a test object by emitting acoustic waves, e.g., ultrasonic waves, to the test object to impart therein acoustic vibrations and analyzing acoustic vibrations reflected from or transmitted through the inside of the test object.
However, in the method of estimating the internal state of the test object by hearing the sound generated when hitting the object, only an experienced person can tell the state by the sound so that only a limited number of persons can take part in the inspection. An apparatus such as an ultrasonic diagnosis instrument visualizes reflected waves from the inside of the test object so that the inner configuration of the test object can be understood. Since, however, the apparatus is configured to detect location information of the inside of the test object, the structure is complicated and expensive.
There is a technology capable of solving the various problems mentioned above by analyzing vibrations generated by hitting the test object using a neural network (see, for example, Japanese Patent Laid-open Application Nos. H7-311185 and 2006-38478).
The Japanese Patent Laid-open Application No. H7-311185 (referred to as 311185 patent hereinafter) discloses a technique, wherein decaying patterns and tone colors of sounds generated when hammering non-defective and defective objects are stored, and the sound generated when impacting a test object is compared with the stored sounds by the neural network to determine whether the test object is defective or not.
Moreover, Japanese Patent Laid-open Application No. 2006-38478 (referred to as 38478 patent hereinafter) discloses a technique that imparts vibrations to the test object without using the impulse hammering and determines whether an amount of characteristics of vibration waveform obtained from a test object corresponds to a non-defective or defective object by using a boundary learning neural network trained by amounts of characteristics of non-defective objects.
According to the 38478 patent, a pattern matching method employed in the boundary learning neural network, wherein an output pattern of group of output units (i.e. neurons of an output layer) corresponding to training samples is compared with a predetermined supervised pattern, then weight coefficients of the output units are compensated based on the difference between the output pattern and the supervised pattern and the neural network is trained by repeatedly compensating the weight coefficients after completing the training an output pattern of group of output units obtained by amounts of characteristics of a test object is compared with the trained output pattern. Further, the training samples are considered as samples having specific differences in the vicinity of an average value so that a distribution of the non-defective object is assumed as a normal distribution.
The 311185 patent discloses the technique of determining whether the test object is non-defective or not based on the hammering sound by using the neural network, but does not disclose a scheme of improving precision of the determination results obtained by using the neural network.
The 38478 patent determines whether the test object is non-defective or not by the pattern matching of output pattern using the neural network, so that the precision of the determination can be improved. However, the distribution of determinations for non-defectiveness or defectiveness is assumed to be the normal distribution, though cracks and breakage generated in test objects can be fairly random, therefore, if the whole non-defective test objects are assumed to have the normal distribution, there is a possibility that a category for the defective objects may be partially overlapped with the one for the non-defective objects.
In other words, if it is assumed that the distribution of the non-defective objects is the normal distribution, a region corresponding to the non-defective category is formed in a convex shape around an average value. Therefore, the technique disclosed in the 38478 patent cannot be employed if the above region corresponding to the non-defective category is partially formed in a concave shape. In other words, the precision determination for non-defectiveness or defectiveness may deteriorate depending upon the nature of a test object.